Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach

Das, Archan; Gómez-Zará, Diego; Contractor, Noshir

doi:10.1007/978-3-030-65351-4_28

Cited by 2 publications

(1 citation statement)

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“…This article is an extended and revised version of a preliminary conference proceeding presented in Complex Networks 2020 [ 43 ]. Compared with the conference article, this version (a) presents a review of team formation algorithms, (b) extends the definitions and pseudo-codes of the proposed team formation problem and algorithm, (c) upgrades the proposed algorithm to handle isolated individuals and when the number of available individuals is not a multiple of the team size, (d) evaluates the algorithm with three datasets to prove that our optimization problem can work in other team formation domains, (e) compares its performance against other benchmark multi-objective algorithms, (f) uses quantitative metrics to compare the algorithms’ results, (g) elaborates on the findings and implications of this work for researchers and practitioners, and (h) provides the scripts to pre-process the datasets, the pre-processed datasets, and the scripts with our proposed algorithm and benchmark algorithms for reproducibility purposes.…”

Section: Introductionmentioning

confidence: 99%

In search of diverse and connected teams: A computational approach to assemble diverse teams based on members’ social networks

et al. 2022

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Previous research shows that teams with diverse backgrounds and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar to them and fail to assemble teams with high diversity levels. We study the team formation problem by considering a pool of individuals with different skills and characteristics, and a social network that captures the familiarity among these individuals. The goal is to assign all individuals to diverse teams based on their social connections, thereby allowing them to preserve a level of familiarity. We formulate this team formation problem as a multi-objective optimization problem to split members into well-connected and diverse teams within a social network. We implement this problem employing the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which finds team combinations with high familiarity and diversity levels in O(n2) time. We tested this algorithm on three empirically collected team formation datasets and against three benchmark algorithms. The experimental results confirm that the proposed algorithm successfully formed teams that have both diversity in member attributes and previous connections between members. We discuss the benefits of using computational approaches to augment team formation and composition.

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Section: Introductionmentioning

confidence: 99%